Abstract

Data-mining techniques have frequently been developed

for Spontaneous reporting databases. These techniques

aim to find adverse drug events accurately and efficiently. Spontaneous reporting databases are prone to missing information,under reporting and incorrect entries. This often results in a detection lag or prevents the detection of some adverse drug events. These limitations do not occur in electronic healthcare databases. In this paper, existing methods developed for spontaneous reporting databases are implemented on both a

spontaneous reporting database and a general practice electronic health-care database and compared. The results suggests that the application of existing methods to the general practice database may help find signals that have gone undetected when using the spontaneous reporting system database. In addition the general practice database provides far more supplementary information, that if incorporated in analysis could provide a wealth of information for identifying adverse events more

accurately.

Item Type:

Conference or Workshop Item
(Paper)

Additional Information:

Published in: Proceedings of the 11th UK Workshop on
Computational Intelligence. Manchester : School of Computer Science, University of Manchester, 2011. http://ukci.cs.manchester.ac.uk/files/Proceedings.pdf

Schools/Departments:

University of Nottingham, UK > Faculty of Science > School of Computer Science